Knowledge that Transforms

Abstract We show that banks with high environmental, social, and governance (ESG) ratings issue fewer mortgages in poor localities—in number and dollar amount—than banks with low ESG ratings. This lending disparity happens at both the county and census tract level, worsens in disaster areas of severe hurricane strikes, is robust to alternative ESG ratings (including using only the social (S) component), and cannot be explained by banks’ differential deposit networks. We find no difference in mortgage default rates between high- and low-ESG banks, rejecting an alternative explanation based on differential credit screening quality. We report a complementary, not substitution, relation between high-ESG banks’ mortgage lending and their community development investments (like affordable housing projects) in poor localities. Loan-application-level analyses confirm that high-ESG banks are more likely than low-ESG banks to reject mortgage loans in poor neighborhoods. The evidence hints at social wash: banks deploy prosocial rhetoric and symbolic actions while not lending much in disadvantaged communities, the social function they arguably ought to perform. Community Reinvestment Act (CRA) examinations partially undo the social wash effect.

Abstract We document that U.S. mutual funds with investment objectives designated as “Sustainable Investment Overall” by Morningstar (ESG funds) are more likely than other mutual funds to vote in support of environmental and social (ES) shareholder proposals and governance (G) shareholder proposals. We also find that the higher support for ES proposals by ESG funds relative to other funds is more pronounced in index funds than in active funds, consistent with trading constraints influencing voting behavior. While these results provide evidence that ESG funds “walk the talk” with their voting behavior on average, we find that fund families play a significant role in that walk. Additionally, in an analysis of fund families that are signatories of the United Nations Principles for Responsible Investment (PRI), we find that ESG funds of PRI families are significantly more likely to support ES proposals and G proposals than non-ESG funds of PRI families. We determine that this significant difference stems from non-ESG funds of PRI families providing less support than non-ESG funds from non-PRI families. Taken together, these results provide evidence that ESG funds available to U.S. investors provide more support for shareholder proposals aligned with their designated investment objective, but the type and family of the fund influence that support.

Abstract We propose the visual attention hypothesis that visuals in firm earnings announcements increase attention to the earnings news. We find that visuals in firms’ Twitter earnings announcements are associated with more retweets, consistent with greater user engagement with announcements that have visuals. This result holds for earnings tweets sent by the same firm and on the same day in firm-level and tweet-level analyses. Consistent with managerial opportunism, firms are more likely to use visuals in their earnings tweets when performance is good but less persistent. Consistent with visuals increasin g investor attention, the initial return response to earnings news is stronger and the post-announcement response is lower when visuals are used. Our evidence of a post-announcement return reversal indicates that visuals can be a double-edged sword. Furthermore, the higher earnings response coefficient from visuals is more pronounced on days with high investor distraction (when many other firms are also announcing earnings). Graphical abstract

Abstract How does artificial intelligence (AI) impact audit quality and efficiency? We explore this question by leveraging a unique dataset of more than 310,000 detailed individual resumes for the 36 largest audit firms to identify audit firms’ employment of AI workers. We provide a first look into the AI workforce within the auditing sector. AI workers tend to be male and relatively young and hold mostly but not exclusively technical degrees. Importantly, AI is a centralized function within the firm, with workers concentrating in a handful of teams and geographic locations. Our results show that investing in AI helps improve audit quality, reduces fees, and ultimately displaces human auditors, although the effect on labor takes several years to materialize. Specifically, a one-standard-deviation change in recent AI investments is associated with a 5.0% reduction in the likelihood of an audit restatement, a 0.9% drop in audit fees, and a reduction in the number of accounting employees that reaches 3.6% after three years and 7.1% after four years. Our empirical analyses are supported by in-depth interviews with 17 audit partners representing the eight largest U.S. public accounting firms, which show that (1) AI is developed centrally; (2) AI is widely used in audit; and (3) the primary goal for using AI in audit is improved quality, followed by efficiency.